Amazon Glue is gaining popularity because many companies have started to use managed data integration services.
ETL is a process that transfers data from a source database to a data warehouse. ETL is complex and difficult to implement for all enterprise data due to its complexity. Amazon introduced AWS Glue to address this problem.
ETL developers and data engineers use Glue to build, monitor, and run ETL workflows.
What is AWS Glue?
AWS Glue, a serverless data-integration service, makes it easy to find, prepare, move and integrate data from multiple sources. This is useful for machine learning (ML) and analytics.
It dramatically reduces the time required to prepare the data for analysis. It automatically finds and lists the data, generates Scala or Python code to transmit the data from the source, and loads and transforms the job according to the timed events.
This allows for flexible scheduling and creates an Apache Spark environment that can be scaled for targeted data loading. In addition, AWS Glue provides complex data stream monitoring and alteration. AWS Glue is a serverless service that simplifies application development’s complicated operations.
It allows for the quick integration of multiple valid data. It also breaks down and authorizes data quickly.
What is AWS Glue used for?
It is important to know the best places to use Amazon Glue. These are just a few examples of AWS Glue uses you should consider.
- Amazon Glue is a tool that allows you to run serverless queries on the Amazon S3 data lakes.
- Amazon Glue is a great tool to get you started. It makes all your data accessible at one interface, allowing you to analyze it without having to move it.
- Amazon Glue can be used to understand your data assets. Amazon Glue makes it easy for you to search different AWS data sets using the Data Catalog. You can also save data across multiple AWS services using the Data Catalog while still having a consistent view.
- Glue can be helpful when building event-driven ETL workflows. You can execute your ETL operations from Amazon S3 by calling your Glue ETL tasks via an AWS Lambda service.
- AWS Glue can also be used to clean, verify, format, and organize data for storage in a data lake or warehouse.
Components of AWS Glue
Below are the main components of AWS Glue:
- Data catalog: This data catalog contains metadata and the data structure.
- Database: This is the key to accessing and creating the database for sources and targets.
- Table: Create one or several tables in the database that are usable by both the target and the source.
- Crawler and Classifier: The crawler retrieves data from the source by using either built-in or custom classifications. It creates/uses pre-defined metadata tables in the data catalog.
- Job: This is the job of business logic to perform an ETL task. This business logic is written internally by Apache Spark using python and scala languages.
- Trigger: An ETL trigger is a device that initiates the execution of an ETL job on-demand or at a particular time.
- Endpoint for development: This creates an environment in which the ETL job script is tested, developed, and debugged.
Benefits of AWS Glue
These are the benefits of using it in your workplace or within an organization.
- AWS Glue scans all data available with a crawler.
- Final processed data can be stored in many places (Amazon RDS and Amazon Redshift, Amazon S3, etc.
- It is a cloud-based service. There is no need to spend money on infrastructures on-premises.
- Because it is a serverless ETL, it is a cost-effective choice.
- It’s fast. It immediately gives you the Python/Scala ETL Code.
Top Features of AWS Glue
Amazon Glue has all the features that you need to integrate data so you can get better insights and use your knowledge to make new advances in minutes instead of months. Here are some of the features that you should know.
- Drag and Drop Interface: A drag-and-drop job editor allows you to create an ETL process. AWS Glue will immediately build the code needed to extract, convert and upload the data.
- Automatic Schema Discovery: To create crawlers that connect to different data sources, you can use the Glue service. It organizes data and extracts relevant information. These data can then be used to monitor ETL processes by ETL tasks.
- Job Scheduling: Glue can either be used on-demand or according to a scheduled schedule. The scheduler can be used to build complex ETL pipelines, establishing dependencies between tasks.
- Code Generation: Glue Elastic Views allows you to easily create materialized views that combine and replicate data from different data sources without having to write any proprietary code.
- Built-In Machine Learning: Glue comes with a built-in Machine Learning feature called “FindMatches”. It deduplicates records that are not perfect copies of each other.
- Developer Endpoints: If you want to actively develop your ETL code, Glue provides developer endpoints that allow you to modify, debug and test the code it creates.
- Glue DataBrew: It is a data preparation tool that can be used by data analysts and data scientists to help them clean and normalize data. It uses Glue DataBrew’s active and visual interface.
How Does AWS Glue Pricing work?
AWS Glue charges an hourly fee, which is billed per second for crawlers (discovering the data) and ETL jobs (processing and loading the data). A simple monthly fee is charged for accessing and storing metadata in the AWS Glue Data Catalog.
Amazon Glue starts at $0.44. You can choose from four plans:
- ETL tasks, development endpoints, and other ETL tasks are available at $0.44
- Crawlers Interactive Sessions are Available at $0.44
- DataBrew jobs start at $0.48
- Monthly storage and requests to the Data Catalog cost $1.00
AWS does not offer a free Glue plan. Each hour will cost $0.44 per DPU. On average, it would cost you $21 per day. Prices can vary depending on where you live.
Steps to Set up AWS Glue
The Data Catalog can be used to quickly find and search multiple AWS datasets without having to move the data. After the data has been cataloged, they are immediately available for query and search using Amazon Athena and Amazon EMR.
- Amazon Redshift, Amazon S3, Amazon RDS, and Databases on Amazon EC2 – Discover your data, store metadata, and use the AWS Glue Data Catalog to discover them
- AWS Glue Data Catalog – Manage data with the data catalog acting as a central repository for metadata
- AWS Glue ETL – Read and write metadata to your data catalog
- Amazon Athena and Amazon Redshift, Amazon EMR, Amazon ETL – Get the data catalog for ETL, analytics, and more.
- Amazon QuickSight – Run reports with Amazon QuickSight, and other business intelligence tools
How to Setup AWS Glue?
Firstly, Sign into the AWS Management Console and open the IAM console. Click on Create role. Then for role type, find Glue, and select Permissions.
I am choosing AWSGlueServiceRole for general AWS Glue Studio and AWS Glue permissions and the AWS-managed policy AmazonS3FullAccess for access to Amazon S3 resources.
Enter a role name.
Click on Create Role.
Create an Amazon S3 bucket.
Create a folder inside the S3 bucket.
Choose the file to upload.
Finally, upload the file in the bucket.
Next, open AWS Glue from the AWS management console and create a database.
Now that you have a database in AWS Glue, create a crawler.
In the data source, select the S3 bucket which you created.
Next, select the IaM role for AWS Glue which you created in the beginning.
Finally, in the output, select
gluedb you created.
Review all the settings and create the crawler.
Once the crawler is created, select it and click on Run. After some time, you will get the status ready.
By running the crawler, the database will get a table with all the data from the CSV file.
When you click on view data, you will be taken to Amazon Athena (query editor). When you run the query, you can see the table data.
Now you can successfully use this AWS Glue crawler in any ETL job.
What is AWS Glue Databrew?
AWS Glue DataBrew allows users to normalize and clean up data without writing any code. DataBrew can reduce the time required to prepare data for machine learning and analytics by as much as 80 percent compared to custom-developed data preparation.
There are over 250 pre-made data transformations that can be used to automate data preparation tasks such as filtering out anomalies, correcting invalid values, and converting data into standard formats.
DataBrew makes it easier for data scientists, business analysts, and engineers to collaborate on extracting insights from raw data. DataBrew is serverless, so you don’t need to manage infrastructure or create clusters to explore and transform terabytes worth of raw data.
DataBrew Features For Enterprises
Visualized Data Preparation
DataBrew is a different way to view data that are typically viewed in columnar databases as alphanumeric numbers. DataBrew visualizes all loaded data sources to help you understand the data relationships and hierarchy.
250+ Data Preparation Automations
Data scientists are expected to follow a variety of repeatable, isolated workflows as part of their job. These workflows and processes have been modeled by AWS as language and data-agnostic module modules. This library includes actions that can be used by end users.
Similar to audit logs that are used to track customer activity in an IT network’s IT network, data lineage allows you to track the data transformation activities within AWS DataBrew. This information includes the data source, the transformations applied, and the data output, including the target location.
Databrew allows you to find matching fields in two data sources. Once matching fields have been identified, they can be loaded into a schema.
AWS Glue DataBrew: Benefits
Below are the features of AWS Glue DataBrew:
- Lower Barrier to Entry for Data Preparation
- Automated Data Profile Generation
- Automate 250+ Data Preparation processes
- Intelligent Prescriptive Suggestions
Alternatives to AWS Glue
Airflow belongs to the Workflow Manager section of a tech stack. It’s an open-source tool that supports GitHub stars, GitHub forks, and other features. Airflow allows you to create workflows using directed acyclic diagrams (DAGs). Airflow scheduler executes your tasks using an array of workers and following the specified dependencies.
Matillion ETL, an ETL/ELT tool, was designed explicitly for cloud databases platforms such as Amazon Redshift and Google BigQuery. It’s a modern browser-based UI with powerful push-down ETL/ELT capabilities. You can be up and running in minutes with a quick setup.
Stitch is an open-source ETL service that connects multiple data sources and replicates data to preferred destinations. It’s very easy to use, as you don’t need any coding knowledge to move data between sources and destinations in Stitch. It is easy to use, has a friendly GUI, and it’s fast.
Stitch doesn’t allow you to choose a pre-made dashboard, unlike other ETL tools. Instead, you must integrate your data into the open data warehouses that you select as a destination. It can be difficult to navigate the inventories.
Alteryx is an analytics automation platform that assists with data collection preparation and blending. This data can be used to speed up processes and provide business insight. Because it’s a drag-and-drop tool, you don’t need any programming knowledge. Alteryx is a great place to go for advice and answers from industry professionals.
So, that was all about AWS Glue, which is a cloud-based solution that allows you to work with ETL pipelines. To sum up, the AWS Glue user interaction process is comprised of three phases. To create a data catalog, you first use data crawlers. Next, you create the ETL code required by the AWS data pipeline. Finally, the ETL schedule is then created. I hope this blog gave you a good overview of Amazon Glue.
You may also explore the best tips to secure AWS S3 storage.
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